Stochastic visual tracking with active appearance models

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Stochastic visual tracking with active appearance models

Hoffmann, McElory Roberto

2009-12

ENGLISH ABSTRACT: In many applications, an accurate, robust and fast tracker is needed, for example in surveillance,
gesture recognition, tracking lips for lip-reading and creating an augmented reality by embedding
a tracked object in a virtual environment. In this dissertation we investigate the viability of a
tracker that combines the accuracy of active appearancemodels with the robustness of the particle
lter (a stochastic process)—we call this combination the PFAAM. In order to obtain a fast system,
we suggest local optimisation as well as using active appearance models tted with non-linear
approaches.
Active appearance models use both contour (shape) and greyscale information to build a
deformable template of an object. ey are typically accurate, but not necessarily robust, when
tracking contours. A particle lter is a generalisation of the Kalman lter. In a tutorial style,
we show how the particle lter is derived as a numerical approximation for the general state
estimation problem. e algorithms are tested for accuracy, robustness and speed on a PC, in an embedded
environment and by tracking in ìD. e algorithms run real-time on a PC and near real-time in
our embedded environment. In both cases, good accuracy and robustness is achieved, even if the
tracked object moves fast against a cluttered background, and for uncomplicated occlusions.